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Amacher, M.C.; O’Neill, K.P.; Conkling, B.L. Watershed erosionprediction project (WEPP) soil erosion rates for forest inventory & analysis (FIA)/Forest Health Monitoring (FHM)) Phase 3 plots in Idaho. In preparation.

Anon. 1995. Sustaining the world’sforests: the Santiago agreement.Journal of Forestry. 93:18-21.

Bull, K.; Stolte, K.W.; Stohlgren,T.W. 1998. Forest health monitor-ing: vegetation pilot field methodsguide. Research Triangle Park, NC:National Forest Health MonitoringProgram. 53 p.

Burgan, R.E.; Rothermel, R.C. 1984.BEHAVE: Fire behavior predictionand fuel modeling system – FUELsubsystem. Gen. Tech. Rep. INT-167.Ogden, UT: USDA Forest Service,Intermountain Forest and RangeExperiment Station. 90 p.

Conkling, B.L.; Coulston, J.W.;Ambrose, M.J. (eds.). 2002. FHM national technical report1991-1999. To be published as aGen. Tech. Rep. Asheville, NC: USDAForest Service, Southern ResearchStation.

Elliot, W.J.; Hall, D.E.; Scheele, D.L. 2000. Disturbed WEPP: WEPP interface for disturbed forest and range runoff, erosion, and sediment delivery. Available athttp://forest.moscowfsl.wsu.edu/fswepp/docs/disweppdoc/html.

Harmon, M.E. and others. 1986.Ecology of coarse woody debris intemperate ecosystems. Adv. Ecol.Res. 15:133-302.

Houston, D.R. Stress Triggered Tree Diseases. The diebacks anddeclines. NE-INF-41-81. USDA Forest Service, Northeastern ForestExperiment Station. 36 p.

McCune, B.; Rogers, P.; Ruchty, A.;Ryan, B. 1998. Lichen communitiesfor forest health monitoring in Colorado, USA. A report to theUSDA Forest Service available athttp://www.wmrs.edu/lichen/pdf%20files/co98_report.pdf

McCune, B.; Dey, J.; Peck, J.;Heiman, K.; and Will-Wolf, S. 1997.Regional gradients in lichen com-munities of the Southeast UnitedStates. Bryologist 100(2): 145-158.

Neitlich, P.N.; McCune, B. 1997.Hotspots of epiphytic lichen diversity in two young managedforests. Conservation Biology 11(1): 172-182.

Pollard, J.E.; Smith, W.D. 2001.Forest health monitoring 1999 plot component quality assurancereport. Research Triangle Park, NC:USDA Forest Service, Forest HealthMonitoring Program.

Smith, W.D.; Conkling, B.L. 2002.Analyzing forest health data. To be published as a Gen. Tech. Rep.Asheville, NC: USDA Forest Service,Southern Research Station.

Stolte K.W.; Duriscoe D.; Cook E.R.;Cline S.P. 1992. Response of western forests to air pollution.Springer-Verlag. New York, NY. 532 p.

Stolte, K.W.; Smith, W.D.; Coulston,J.W. [and others]. 2002. FHMnational technical report 1991-1998. Gen. Tech. Rep. SRS-xxx.Asheville, NC; U.S. Department of Agriculture, Forest Service, Southern Research Station.

R E F E R E N C E S

The U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs and activities on thebasis of race, color, national origin, sex, religion, age, disability, political beliefs, sexual orientation, or maritalor family status. (Not all prohibited bases apply to all programs.) Persons with disabilities who requirealternative means for communication of program information (Braille, large print, audiotape, etc.) shouldcontact USDA’s TARGET Center at (202) 720-2600 (voice and TDD).

To file a complaint of discrimination, write USDA, Director, Office of Civil Rights, Room 326-W,Whitten Building, 1400 Independence Avenue, SW,Washington, DC 20250-9410 or call (202) 720-5964 (voice andTDD). USDA is an equal opportunity provider and employer.

Forest Health IndicatorsForest Inventory and Analysis Program

United States Department of Agriculture

Forest Service

FS-746October 2002

AU T H O R S

Kenneth StolteResearch ScientistUSDA Forest ServiceSouthern Research StationResearch Triangle Park, NC

Barbara ConklingResearch Asst. ProfessorNorth Carolina State UniversityResearch Triangle Park, NC

Sally CampbellBiological ScientistUSDA Forest ServiceNational OffceArlington, VA

Andrew GillespieNational Program ManagerForest Inventory and AnalysisUSDA Forest ServiceNational OfficeArlington, VA

AC K N O W L E D G E M E N T S

The authors gratefullyacknowledge the followingpeople for their review and contributions to thisbrochure:

Mike AmacherFaith CampbellAlan LucierManfred MielkePeter NeitlichKathy O’NeillMargaret PetersenJennifer PlylerJim PollardMike SchomakerBeth SchulzGretchen SmithKaren WaddellSuzi Will-Wolf

Forest Inventory and Analysis. The Forest Service Forest Inventory and Analysis (FIA) Pro-gram is the Nation’s continuous forest census.Since 1930, we have collected, analyzed, and

reported information on the status and trends of America’s forests: how much forest exists, where it exists, who owns it, and how it is changing – growing, dying, or being harvested. In response towidening customer interests, the FIA Program isdeveloping a core program that will be implementedin the same manner on all U.S. forest lands. Itincludes sampling an extended suite of forest healthindicators. The purpose of this brochure is todescribe these health indicators: what we are measuring, why we believe these measurements areimportant, how we collect and interpret the data, andexamples of what we have found to date. The FIAindicators discussed in this brochure are:

■ Crown Condition ■ Lichen Communities■ Ozone Injury ■ Down Woody Debris■ Tree Damage ■ Vegetation Diversity and Structure ■ Tree Mortality ■ Soil Condition

These indicators were developed and initiallymeasured by the Forest Health Monitoring (FHM)Program in the 1990s. In 1999, they weretransferred to FIA and now are a subset of the FIAsample grid (one forest health plot for every 16 standard FIA plots). On the forest health plots, coreFIA measurements (for example, height, diameter,species) are made, as well as the forest health measurements.

32

I N T R O D U C T I O N

F

FIA Customers and Data Users. The FIA Program has adiverse and growing set of customers that are inter-ested not only in reports and analyses produced byFIA, but also in using the actual FIA data itself. Con-gress, State legislators, and State foresters have longused FIA information as a basis for formulatingsound forest policy. Forest industry, environmentalorganizations, and a wide range of private consult-ants use FIA information for business planning.Government and academic researchers use FIA dataas a basis for further research. And land managers atall levels are relying on FIA data for a strategic lookat forest resources and as a basis for forest planningat the strategic scale. All of these users haveexpressed interest in the broader suite of FIA meas-ures that includes indicators of forest health.

FIA also contributes data and analyses to a varietyof national and global assessments. FIA data addressat least 38 of the 67 Criteria and Indicators of Sus-tainability for reporting under the Montreal Process.FIA data are key for producing the reports requiredby the Resource Planning Act and are increasinglybeing used to support regional resource assessmentsused as a basis for forest planning.

Researchers and policymakers are using FIA dataabout forest soils, down woody debris, and tree bio-mass to estimate carbon budgets and to model thepotential for carbon sequestration under differentmanagement scenarios. The fire management com-munity is using the forest structure and down woodydebris data to identify areas at highest risk of cata-strophic fire and opportunities for preventativetreatments. Land managers use understory vegeta-tion data to track increases of invasive species. Manypartners use FIA data on ozone injury, lichen com-munity composition, tree damage, and tree crowncondition to report, as required by several interna-tional treaties, on overall forest health and probableimpacts of air quality and acid deposition.

Brochure Format. The majority of this brochure isdevoted to descriptions of each forest health indica-tor. Each indicator is featured on two facing pageswith the following sections:

■ What is the indicator?■ Why is it important?■ How is it measured?■ How can the data be used?

More Information. For more detailed information aboutthe FIA Program and the forest health indicators,including field methods, please visit the FIA Web siteat http://fia.fs.fed.us. Information about the FHM Pro-gram is available at http://www.na.fs.fed.us/spfo/fhm/.

54

FIA subplot (crowns, damage, mortality, & vegetation measured) - 24.0 ft radius area

Liche sampling area - 120.0 ft radius area

Vegetation sampling quadrat - 1.0 m2 area

Soil sampling area - Point samples

Down woody debris transect - 24 ft transects

FIA Plot Design for Forest Health Indicators

What Is the Crown Condition Indicator? The crown indica-tor is based on the amount, condition, and distributionof foliage, branches, and growing tips of trees (seephoto below). There are five components of the crownindicator:

■ Ratio – the length of the tree with branches, divid-ed by the total tree height.

■ Density – a visual estimate of the fullness of thecrown, based on the amount of skylight blockedfrom view by leaves, branches, bole, and fruits.

■ Foliar transparency – an estimate of the amount of skylight seen in the foliage parts of branches.

■ Dieback – the death of the sun-exposed, growingtwigs in the upper crown.

■ Diameter – the width of the tree crown. It is estimated with models.

Why Is the Crown Indicator Important? In addition tobeing aesthetically pleasing, tree crowns provideshade, temperature moderation, and food and habitatfor many organisms. They also are a source of fuelfor wildfires. Crowns give a quick evaluation of gen-eral tree health. Healthy, full crowns suggest carbonis being stored; the tree is growing; and there are noserious impacts from pathogens, air pollutants, orinsects.

How Is the Crown IndicatorMeasured? Two peoplestand – at different angleswith the most open viewsof the crown – one tree-length away from the treebeing evaluated. Visualestimates of each crownindicator are recorded inpercentage classes (0-5percent, 5-10 percent,…95-100 percent) foreach tree. Consensus isreached on each indicatorusing visual guides forstandardization.

How Can Crown IndicatorData Be Used?The crown condition indi-cator is a good generalindicator of tree healthand can be used to:

■ Monitor visible changesin crown condition

■ Relate crown conditionto tree growth orresilience to stressorssuch as insects and diseases.

■ Evaluate suitability ofstand for wildlife andbird species.

■ Provide data toestimate risk of cata-strophic crown fires.

■ Serve as an indicatorof soil fertility and toxicity.

■ Serve as an indicator of climate change

F I A F O R E S T H E A L T H I N D I C A T O R S

Crown Condition

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High foliage transparency(left arrow) and dieback(right arrow) of a pine treeunder stress; note healthycrowns of other pines.

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Softwood dieback:1999 status

2.51 - 55.01 - 7.57.51 - 1010.01 - 18.83Ecoregion section boundaryState boundary

Relationship between foliar transparency and growth of pines.1993-99 data. Foliar Transparency ≥20 percent was associated with reduced growth.

Dieback on Softwoods in NortheasternUnited States. By ecological units, 1999data.

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What Is the Ozone Injury Indicator? A number of plantspecies are sensitive to ozone exposures above nor-mal background levels. These bioindicator speciesexhibit an upper surface foliar injury symptom thatcan easily be distinguished from other foliar injuriesand quantified. Ozone bioindicator species are moni-tored in all U.S. forests.

Why Is the Ozone Injury Indicator Important? Elevatedozone exposures are an air-quality problem that hasgreat potential to affect human health, as well as for-est health and productivity over large areas. Injury tobioindicator plants shows us where ozone exposuresin forests are high enough to cause plant injury, andgives estimates of the relative severity of exposures.They also show where the highest probability ofreduced growth; reduced resilience to insects,pathogens, drought, and other stressors; andincreased mortality might be occurring.

How Is the Ozone Injury Indicator Measured? The ozoneindicator is not sampled on FIA plots because specif-ic site and plant species requirements must be metthat may not be present on the FIA plot. Ozone bio-monitoring sites are located in forest openings wherethree or more bioindicator species are found. Inforests where ozone exposures are very high and sen-sitive species are common, increased numbers ofsites are located. The plants are rated for foliarinjury symptoms during the peak of ozone injury(mid-to-late summer), before fall colors mask ozonefoliar symptoms. The severity of foliar injury isexpressed in an Ozone Injury Score (OIS) – 0 to 4.9

(no or minute injury), 5 to 24.9 (low to moderateinjury), and greater than 25 (severe injury). FIA ana-lysts use consecutive 5-year periods with variableozone exposures, weather, wind flow, and precipita-tion patterns to examine regional trends in OIS overthe long term.

How Can Ozone Injury Data Be Used? Ozone injury datacan identify forested areas where ozone exposuresare high and where other necessary conditions forinjury (e.g., adequate light, nutrition, and moisture)are also present. Ozone injury data can be used to:

■ Identify areas in forests where ozone injury ishigh.

■ Relate the severity of ozone injury to changes ingrowth, mortality, or diversity.

■ Relate the severity of ozone injury to increasedsensitivity to biotic and abiotic stressors.

■ Relate the severity of ozone injury to changes inanimals or insects dependent on ozone-sensitivebioindicator species (for example, the Monarchbutterfly is dependent on common milkweed, abioindicator species).

F I A F O R E S T H E A L T H I N D I C A T O R S

Ozone Injury

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Severe ozone injury on tulippoplar leaf.

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Ozone Index Scores in Northeastern United States. By ecological unit, 1994-99 data.

Ecoregion ozone index

0 - 55 - 2525+Ecoregion section boundaryState boundary

What Is the Damage Indicator? The damage indicator isthe measurement of the type, location, and severityof injury caused by diseases, insects, storms, andvarious human activities. Some examples of damagesrecorded are open wounds, signs of advanced decay,cankers, and broken boles, roots, or branches.

Why Is the Damage Indicator Important? Damage causedby a variety of natural and human-caused sourcesaffects growth and development of trees. Identifyingunexpected or unexplained damage is intended totrigger further investigation into causal agents andtree responses. Information from this indicator shouldhelp answer questions about roles of biotic and abiot-ic stressors and how they affect biological conditionsand processes within the forest community.

How Is the Damage Indicator Measured? Damage dataare collected on all FIA plots. A maximum of two different damages may be recorded per tree. Individ-ual damages are characterized by type or kind ofdamage, the location on the tree where the damageoccurs, and the severity of the damage.

In general, damages are considered more seriouswhen they are found on lower parts of the tree andlarge areas of tree are affected. Damages must meetminimum thresholds to be recorded. Therefore, zero

damage recorded means there were no damagesobserved that met the minimum injury threshold.Cause or causes of damage are not collected as partof the core damage measurements. Methods formatching causal agents with observed damage anduseful data sources are under discussion in FIA.

How Can Damage Data Be Used? Damage data are col-lected to:

■ Assess the damage severity index (DSI) for eachplot. DSIs are calculated from the three damagecomponents: type, location, and severity. In gener-al, a high DSI indicates multiple damages, severetypes of damage, and/or a lot of damage occurringnear the bottom of the tree. DSI values less than15 indicate trees with minimal damage that arelikely to be a normal occurrence in fairly healthystands. The DSI results are usually separated bytree species because of the biological differencesbetween species.

■ Evaluate what forests or proportions of forests areat risk of reduced growth and/or increased mortal-ity due to the damages observed.

■ Determine how the life, development, or survivalof the forest is affected by the level of damagesobserved.

F I A F O R E S T H E A L T H I N D I C A T O R S

Tree Damage

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Damage Severity Index (DSI)for hardwoods in the GreatLakes area. By ecologicalunits, 1999 data. Furtheranalysis is needed to definerelationships between DSIand growth or mortality.

Percentage of plots having mean plot hardwood DSI of 15 or greater

0 - 1010.1 - 1020.1 - 3030.1 - 50Ecoregion section boundaryState boundary

Open wound on sweetgumtree caused by beavers.

ment cycle to the next.The cause of the mortalityis recorded when it can be determined.

Mortality data are most meaningful whencompared to growth datafrom the same plots. Thevolume of trees that died –mortality volume (mv) – is divided by the volumegained in growth (gv) ofthe live trees. A mv/gvvalue greater than 1.0means there was a netloss of wood volume onthe plot. A related methodcompares the average size(diameter) of the treesthat have died (dd) to the size of remaining livetrees (ld). A dd/ld ratiogreater than 1.0 meansthe average size of deadtrees is larger then theaverage size of livingtrees.

How Can Tree Mortality Indicator Data Be Used?The tree mortality indicatormeasures a key forestprocess and therefore hasa number of uses. It canidentify:

■ High mortality areas – FIA plot data can be usedwith insect and disease survey data to betterdetermine the geographic extent of mortality, tracktrends, and determine causes.

■ Habitat suitability for wildlife.■ Areas of high fuel loading and fire spread risk.■ Carbon cycling patterns.■ Sustainability of stand developmental patterns.■ Climate change.

Annual mortality volume as a percentage of gross volume growth

0 - 1010.1 - 3030.1 - 6060.1 - 9090.1 - 104Insufficient DataEcoregion section boundaryState boundary

What Is the Tree Mortality Indicator? The tree mortalityindicator is the number, size, and volume of treesthat have died since the previous measurement on anFIA plot. It provides information on whether changesin abiotic or biotic stressors or stand developmentare creating conditions less favorable for tree growthand survival.

Why Is the Tree Mortality Indicator Important? Mortalityis an essential part of all healthy forest ecosystems.It contributes to ecosystem functioning and diversityby creating dead material for nutrient recycling, cre-ating openings that result in mosaics of species andages, and providing habitat for wildlife. Mortality mayalso reduce productivity of forests being managed for wood production and increase risk of wildfire.Changes in the rates and amounts of mortalityrequire close scrutiny to separate “normal” oracceptable levels of mortality from unusual or unac-ceptable levels. Analysis of affected species, theirages, disturbance history in the area, and the causeof mortality are essential information for land man-agers and policymakers.

How Is the Tree Mortality Indicator Measured? Tree mor-tality is the measurement of diameter and height oftrees that have died on FIA plots from one measure-

F I A F O R E S T H E A L T H I N D I C A T O R S

Tree Mortality

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Port-orford-cedar killed by aroot disease in southwesternOregon.

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Mortality in southern Oregon andnorthern California. By ecologicalunits, 1992-99 data. The cause ofthe high mortality in coastal northern California is currentlyunder review.

What Is the Lichen Communities Indicator? Lichens are anassociation of fungi and algae that grow on a varietyof substrates including trees. They have a number ofimportant forest functions, including cycling of nutri-ents and providing forage and habitat for a variety of wildlife. Because of their physiology, lichens areresponsive to environmental stresses such as air pollution and climate change, making them useful asindicators of both. They also are indicators of forestecosystem biodiversity.

Why Is the Lichen Communities Indicator Important?Lichen community composition and changes in com-position can provide information about changes in airquality, climate, and biological processes. For exam-ple, not finding a pollution-sensitive species in anarea where it is expected can indicate the presenceor past presence of pollutants. Finding many nitro-gen-loving species in an area can indicate highnitrogen deposition, which may also affect other com-ponents of the ecosystem, such as water quality.Diversity and abundance of lichens is also affected byforest type, age, density, and spatial arrangement oftrees and other vegetation.

How Are Lichen Communities Measured? The indicatorhas two data collection parts – collecting samples forspecies identification (to determine number of differ-ent species per plot) and estimating speciesabundance (the number of times each speciesoccurs). Samples are collected from woody plants(conifers and hardwood trees and shrubs above 0.5

meters) and recently fallen trunks and branches.Samples are mailed to an expert for identification.Abundance within the lichen plot is recorded for eachspecies. Other data are recorded to describe the plotsuch as percent canopy cover, percent gaps, anddominant tree and shrub species. This information iscombined with FIA tree data, environmental data, andmeasures of lichen community composition to suggestcauses for variations in the lichen communities.

How Can Lichen Data Be Used? Two main uses of lichenindicator data are determining lichen abundance anddiversity and identifying the relationships amonglichens, climate, and air pollution. Lichen communitydata can be used to:

■ Determine species abundance and richness by plot.■ Determine regional diversity – number of species

recorded in a large region, such as an ecoregionsection.

■ Develop biotic indexes – indexes based on lichencommunity data from plots along pollution and climate gradients across large regions. Gradientmodels have been developed for Colorado and theSoutheastern United States. Models for other loca-tions are under development.

F I A F O R E S T H E A L T H I N D I C A T O R S

Lichen Communities

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Pollution-sensitive lichen,Lobaria pulmonaria.

Lichen richness in Southeastern United States. By plot, 1993-98 data. Analysis and use of gradient models help determine if lower species richness in these Statesis due to air pollution, climate, forest structure, or other causes. North and SouthCarolina were measured in 1999; their measurements will expand the analysis inthis region.

Plot lichen species richness1 - 78 - 1314 - 1920 - 2526 - 32No lichens on plotEcoregion section boundaryState boundary

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What Is the Down Woody Debris Indicator? The downwoody debris (DWD) indicator is the measurement of fallen trees, dead branches, and large fragments of wood on the forest floor. Small branches less than3 inches in diameter are measured as fine woodydebris, while larger branches and down trees aremeasured as coarse woody debris. DWD records thespecies, shapes, sizes, holes, and stages of decay.

Why Is the Down Woody Debris Indicator Important?Down woody debris is an important element of pro-ductive and biologically diverse forests. It is animportant component of forest productivity, wildlifehabitat, fuel loading, soil erosion, and carbon stor-age. As DWD decomposes, the soil is enriched withorganic matter, nutrients, and moisture. Larger downtree boles provide dens for wildlife. Both large andsmall debris provide shelter and food for insects; ger-mination sites for tree, herb, and shrub species; asubstrate for fungi and microorganisms; and long-term storage for water, carbon, and other nutrients.

How Is the Down Woody Debris Indicator Measured? Threetransect lines on each of four subplots radiate outfrom subplot center and extend to subplot edge. Alldown wood greater than 3 inches in diameter crossedby the transect line are measured for bole diameter,length, stage of decay, species, and presence of hol-low cavities. Fine woody debris is recorded as thenumber of pieces less than 3 inches in diameter indifferent size classes. Large piles of coarse woodydebris are treated as a unit based on the size andshape of the pile.

How Can Down Woody Debris Data Be Used? DWD datarelate to a number of forest ecosystem processes andhence provide multiple and varied uses in analyzingforest health. DWD data can help evaluate:

■ Biomass, carbon, and nutrient storage.■ Fuel loading and fire spread – down woody debris

fuels can be classed by species groups, state-of-decay groups, and log sizes.

■ Wildlife habitat – type and amount of down woodavailable or used as habitat.

■ Soil stabilization.

F I A F O R E S T H E A L T H I N D I C A T O R S

Down Woody Debris

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Biomass and carbon in coarse woody debris in western Oregon.1991 – 98 data. Average biomass and carbon was highest in hemlock forest types.

Down logs and smallerwoody debris.

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Total coarse fuels in Oregon and Washington. 1998 pilot study data. Coarsewoody debris is highly variable from plot to plot due to factors such as forest type,climate, and disturbance history.

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What Is the Vegetation Indicator? The vegetation indica-tor measures the type, abundance, and verticalposition of vascular plant species on FIA foresthealth plots. It inventories and monitors the status,changes, and trends in plant species richness andabundance, and the vertical structure of the forestvegetation.

Why Is the Vegetation Indicator Important? The vegeta-tion indicator produces information on native plantdiversity; spread of invasive exotic species; fuel load-ing and fire-spread rates; food and habitat forwildlife; carbon cycling; and the impacts of acid rain,nitrogen deposition, ozone, and climate changeeffects. It will be useful in evaluating the risk of soilerosion, resistance and recovery from disturbance,and providing data to type plot vegetation by locallydefined communities.

How Is the Vegetation Indicator Measured? Vegetation ismeasured using a multiple-scale nested approach. Alltrees, shrubs, herbs, grasses, vines, ferns, and fernallies are identified and quantified on three perma-nently positioned 1m2 quadrats on each subplot. Thegeneral characteristics of the forest floor, such aslitter, moss, bare ground, and rocks are also record-ed. The rest of the subplot is then searched for

additional species. For each species on the subplot,cover is estimated and the vertical layer (0-2, 2-6, 6-16, >16 ft) where the majority of the foliage occursis recorded. Unknown species are collected near theplot, then pressed, dried, labeled, and mailed to aqualified herbarium for identification.

How Can Vegetation Indicator Data Be Used? The vegeta-tion indicator data can be used in many ways. Someof the most common uses will be to:

■ Monitor native diversity in the understory.■ Track the spread of exotic invasive plants and

impacts on native species.■ Relate vegetation changes to soil chemical and

physical changes, ozone exposures, and climatechange.

■ Evaluate amount, extent, and suitability of wildlifehabitat.

■ Provide data for fuel loading and fire spread risk.

F I A F O R E S T H E A L T H I N D I C A T O R S

Vegetation Diversity and Structure

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Spread of invasivespecies. 1998-99 pilot studydata. A model was used todetermine the likely origin ofinvasive species (invasivehotspot) and areas where thespecies is next likely to spread(early detection plots).Understory plant diversity

and vertical layering in amixed hardwood-pine forest.

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What Is the Soil Condition Indicator? The soil conditionindicator is designed to collect data about erosion,compaction, and important physical and chemicalsoil properties. These data establish baseline valuesthat can be used to characterize soil status on allforest land across the United States, and can be usedwith subsequent measurements to identify and evalu-ate change. The chemical and physical propertiesinclude nutrient information, such as the amount ofexchangeable cations (e.g., calcium, magnesium,potassium, and phosphorus); pH level; carbon andnitrogen; toxics such as heavy metals; and the bulkdensity (weight of soil per unit volume).

Why Is the Soil Condition Indicator Important? Soils areimportant to forest land because vegetation dependson the five basic environmental factors that soils pro-vide – nutrients, water, air, heat, and mechanicalsupport. Chemical and physical data provide informa-tion about the physical and fertility status of forestsoil and are used in many ecological models at differ-ent spatial scales of the forest ecosystem, such as inestimates of the forest carbon budget.

How Is the Soil Condition Indicator Measured? Soil meas-urements are made in both the field and laboratoryand are divided into three categories – soil erosion,compaction, and soil sampling. Samples for analysisare collected from the forest floor (litter layer and O-horizon) and from underlying mineral soil in two

equal increments down to about 8 inches. These soilsamples are analyzed for chemical and physical prop-erties in laboratories.

How Can Soil Condition Indicator Data Be Used? There arethree main components to the data:

■ Chemical and physical information—to evaluatecharacteristics, such as fertility; potential of thesoil system to accommodate stressors, such asacid precipitation or heavy metals; bulk density;and carbon. The ability to measure soil carboncontent consistently on a national set of forestplots will contribute greatly to current informationabout how forest soils store and release carbon.

■ Soil erosion information—to assess the potentialfor accelerated erosion in response tomanagement practices or naturally occurringevents. For example, the Watershed Erosion Pre-diction Project (WEPP) model (Elliot and others2000), currently being evaluated for use with thesoils plot data, makes erosion predictions for dif-ferent forest ages and disturbance types.

■ Soil compaction information—to assess the statusand changes in the amount of compaction and thetype observed, such as ruts, trails, or areas. Com-paction can be caused, for example, by repeatedpassage of heavy machinery or vehicles.

F I A F O R E S T H E A L T H I N D I C A T O R S

Soil Condition

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Soil sample collection.

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Mean organic carbon concentrations in forest floor soils of the Western UnitedStates. By plot, 1998 and 1999 data. Plot to plot differences can be caused byclimate, forest, and understory vegetation.

% Organic Carbon (by weight)< 25%25 - 35%35 - 45%> 45%State Boundaries

What Is the Quality Assurance Program? The FIA continu-ous quality management program is designed tocontrol, correct, and document measurement uncer-tainty. This process utilizes quality control (QC) andquality assurance (QA) techniques to continuallyimprove field and laboratory measurements. The pro-gram includes extensive training, auditing, and timelyfeedback to field crews. FIA is also working toinclude the QA process in information management,data analysis, and reporting. The overall QA programhas many components, including a carefully planned,executed, and documented training program for eachcrew member. The training program requires crewmembers to pass both written and field tests in orderto be certified to collect data.

Two main kinds of checks or audits are done dur-ing data collection: (1) those done by a qualified QAcrew that has the data collected by the field crewsand checks it in the field themselves (field crew mayor may not be present during the check) and (2) aqualified QA crew performs a complete plot reinstal-lation and remeasurement of a previously measuredplot (field crew is not there and does not know whichplot will be remeasured).

Why Is QA Important? The QA checks provide differentkinds of information to help improve data collectionand to document the quality of the data collected.This information helps FIA analysts determine if dataare appropriate for particular analysis techniquesand models.

How Are QA Data Used? One use of the complete plotreinstallation and remeasurement data is performanceevaluation of the crews. The absolute magnitude of thedifferences (bias) between field crews and QA crews iscompared to measurement quality objectives specifiedfor each measurement. For example, in 1999, theaverage percent of trees measured that met the meas-urement quality objectives in all regions for all crownmeasurements (hardwoods and softwoods) was about89 percent, with values ranging from 67 percent to100 percent. These analyses help in data interpreta-tion and in identifying problems in data collection thatthen leads to solutions.

Bioindicator plant – A plant thatexhibits a unique, detectable,visible injury response whenexposed to a particular stressor.

Core measurements – Measure-ments that must be made on allFIA plots using the FIA FieldMethods Guide procedures.

Ecological unit – An area charac-terized by similar climate,soils, and topography. Groupedat various spatial scales, suchas region, domain, province,section, land unit, and landtype.

Exotic species – Nonnativespecies of plants or animalsthat often are invasive, compet-ing with native species forspace, nutrients, or light.

Forest land – Land that is atleast 10 percent stocked byforest trees of any size, includ-ing land that formerly had suchtree cover and that will be naturally or artificially regenerated.

FIA plot design – A fixed-areaplot where most FIA measure-ments are collected withoutdamage to the plot, and datacan be stratified in analysis byforest types, disturbances, etc.

Hardwood – A dicotyledonoustree, usually broad-leaved anddeciduous.

Lichen – Nonvascular organismthat is a symbiotic combinationof a fungus and algae. The fungus attaches to a variety ofsubstrates such as trees, rocks,and soil. The algae get nutrients and water directlyfrom the air and makes sugarfor growth of both.

Nonforest land – Land that doesnot support or has never supported forests, and landsformerly forested where use fortimber management is preclud-ed by development for otheruses.

pH (soil) – The degree of acidityor alkalinity of a soil.

Quality assurance – The overallsystem of management activi-ties designed to assure qualitydata.

Quality control – Operationaltechniques and activities thatare used to control the dataacquisition process.

Softwood – A coniferous tree,usually evergreen, having needles or scale-like leaves.

Stressor – An agent that causesa plant to suffer damage orstress or be killed. Stressorscan be living (biotic) – such asinsects, diseases, mammals, or competing vegetation – or nonliving (abiotic) – such aschemical pollutants or weatherextremes.

Tree – An individual of a treespecies with a diameter atbreast height or diameter atthe root collar that is greaterthan or equal to 5.0 inches.

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Amacher, M.C.; O’Neill, K.P.; Conkling, B.L. Watershed erosionprediction project (WEPP) soil erosion rates for forest inventory & analysis (FIA)/Forest Health Monitoring (FHM)) Phase 3 plots in Idaho. In preparation.

Anon. 1995. Sustaining the world’sforests: the Santiago agreement.Journal of Forestry. 93:18-21.

Bull, K.; Stolte, K.W.; Stohlgren,T.W. 1998. Forest health monitor-ing: vegetation pilot field methodsguide. Research Triangle Park, NC:National Forest Health MonitoringProgram. 53 p.

Burgan, R.E.; Rothermel, R.C. 1984.BEHAVE: Fire behavior predictionand fuel modeling system – FUELsubsystem. Gen. Tech. Rep. INT-167.Ogden, UT: USDA Forest Service,Intermountain Forest and RangeExperiment Station. 90 p.

Conkling, B.L.; Coulston, J.W.;Ambrose, M.J. (eds.). 2002. FHM national technical report1991-1999. To be published as aGen. Tech. Rep. Asheville, NC: USDAForest Service, Southern ResearchStation.

Elliot, W.J.; Hall, D.E.; Scheele, D.L. 2000. Disturbed WEPP: WEPP interface for disturbed forest and range runoff, erosion, and sediment delivery. Available athttp://forest.moscowfsl.wsu.edu/fswepp/docs/disweppdoc/html.

Harmon, M.E. and others. 1986.Ecology of coarse woody debris intemperate ecosystems. Adv. Ecol.Res. 15:133-302.

Houston, D.R. Stress Triggered Tree Diseases. The diebacks anddeclines. NE-INF-41-81. USDA Forest Service, Northeastern ForestExperiment Station. 36 p.

McCune, B.; Rogers, P.; Ruchty, A.;Ryan, B. 1998. Lichen communitiesfor forest health monitoring in Colorado, USA. A report to theUSDA Forest Service available athttp://www.wmrs.edu/lichen/pdf%20files/co98_report.pdf

McCune, B.; Dey, J.; Peck, J.;Heiman, K.; and Will-Wolf, S. 1997.Regional gradients in lichen com-munities of the Southeast UnitedStates. Bryologist 100(2): 145-158.

Neitlich, P.N.; McCune, B. 1997.Hotspots of epiphytic lichen diversity in two young managedforests. Conservation Biology 11(1): 172-182.

Pollard, J.E.; Smith, W.D. 2001.Forest health monitoring 1999 plot component quality assurancereport. Research Triangle Park, NC:USDA Forest Service, Forest HealthMonitoring Program.

Smith, W.D.; Conkling, B.L. 2002.Analyzing forest health data. To be published as a Gen. Tech. Rep.Asheville, NC: USDA Forest Service,Southern Research Station.

Stolte K.W.; Duriscoe D.; Cook E.R.;Cline S.P. 1992. Response of western forests to air pollution.Springer-Verlag. New York, NY. 532 p.

Stolte, K.W.; Smith, W.D.; Coulston,J.W. [and others]. 2002. FHMnational technical report 1991-1998. Gen. Tech. Rep. SRS-xxx.Asheville, NC; U.S. Department of Agriculture, Forest Service, Southern Research Station.

R E F E R E N C E S

The U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs and activities on thebasis of race, color, national origin, sex, religion, age, disability, political beliefs, sexual orientation, or maritalor family status. (Not all prohibited bases apply to all programs.) Persons with disabilities who requirealternative means for communication of program information (Braille, large print, audiotape, etc.) shouldcontact USDA’s TARGET Center at (202) 720-2600 (voice and TDD).

To file a complaint of discrimination, write USDA, Director, Office of Civil Rights, Room 326-W,Whitten Building, 1400 Independence Avenue, SW,Washington, DC 20250-9410 or call (202) 720-5964 (voice andTDD). USDA is an equal opportunity provider and employer.

Forest Health IndicatorsForest Inventory and Analysis Program

United States Department of Agriculture

Forest Service

FS-746October 2002